Patchy and Pink: Dynamics of a Chlainomonas sp. (Chlamydomonadales, chlorophyta) algal bloom on Bagley Lake, North Cascades, WA

Abstract Snow algal blooms frequently occur throughout alpine and polar environments during spring and summer months; however, our understanding of bloom dynamics is limited. We tracked a recurrent bloom of Chlainomonas sp. on Upper Bagley Lake in the North Cascade Mountains, USA, to assess the spatiotemporal dynamics in bloom color intensity, community photophysiology, and community composition over eight weeks. We found that the algae biomass had a dynamic patchy distribution over space and time, which was decoupled from changes in community composition and life-cycle progress averaged across the bloom. The proportional representation of Chlainomonas sp. remained consistent throughout the study while the overall community composition shows a progression through the bloom. We found that community photophysiology, measured by the maximum quantum yield of PSII (Fv/Fm), decreased on average throughout the bloom. These findings suggest that the Chlainomonas sp. community on Bagley Lake is not simply an algal bloom with rapid increase in biomass followed by a population crash, as is often seen in aquatic systems, though there is a physiological trajectory and sensitivity to environmental stress. These results contribute to our understanding of the biology of Chlainomonas sp. and its response to environmental stress, specifically an extreme warming event.


Introduction
Snow algae, a group of closely related species in the phylum Chlor ophyta, ar e ada pted to liv e and bloom in snow habitats and grow and photosynthesize at near-freezing temperatures.Algal biomass can turn snow surfaces pink, red, orange, and green during bloom e v ents due to the dominant pigments in the cells .T hese blooms are important in the cryosphere, because they reduce the reflectance of solar radiation off snow surfaces, which accelerates snowmelt in snowpacks and glaciers that are currently alr eady melting r a pidl y due to climate warming (Yallop et al. 2012, Ganey et al. 2017, Hotaling et al. 2021 ).Snow algal blooms are a seasonal phenomenon and onl y a ppear in spring and summer months when there is liquid water available in snow to facilitate growth and reproduction (Hoham 1974a, Hoham and Mullet 1977, Ling and Seppelt 1998, Hoham 2000, Kvídero vá 2010 ).T hough the seasonal a ppear ance of sno w algae in polar and alpine sno w habitats r eliabl y ha ppens e v ery year, ther e ar e still man y open questions about the mechanisms by which snow algae repopulate the snow annually and how blooms initiate.
Though no single parameter is used to define what is considered a "snow algal bloom," it is gener all y known to be an e v ent that is defined by r elativ el y high biomass concentrations compared to seasonal averages and is typically time-limited (Smayda 1997 ).In aquatic systems, blooms are understood to be events that produce abundant biomass that appears in a r elativ el y short period of time.In contrast, the persistence of high biomass can have v ariable dur ations (Sv er drup 1953 ).In sno w, algal blooms are typically described as the appearance of visible biomass on snow surfaces that turns the snow pink, red, orange, or green due to the abundant pigments found in cells .T here is a connection between the intensity of color on snow surfaces and snow algae biomass, where higher biomass produces darker colors.Ho w ever, this relationship is not well c har acterized or constrained.In places where snow algae bloom on seasonal snowpack, biomass often persists until the habitat disappears or until it is buried by new snow accumulation at the end of the growing season.
Despite the well-documented phenomenon of snow algae blooms on snow surfaces worldwide in alpine and polar environments, c har acterization of bloom pr ogr ession acr oss a gr owing season is limited.There are two primary ways to c har acterize the pr ogr ession of snow algal blooms over time: (i) observation of color (i.e., biomass) on snow surfaces and (ii) c har acterization of the snow algal biology via microscopy , physiology , molecular tools, and other biological methodologies.Because snow algal blooms are so visible on snow surfaces, observation and quantification of color can be used as a measure of bloom area and can be correlated with snow algal biomass.Many kinds of remote sensing technologies have been used to observe snow algal blooms on snow surfaces, in an attempt to quantify bloom size and impact on melt (Painter et al. 2001, Gray et al. 2021, Engstr om et al. 2022 ).Recentl y, r emote sensing has been used in the Pacific Northwest region of North America to show changes in snow algal biomass over time on glaciers (Engstrom et al. 2022 ).While remote sensing methods are just starting to attempt to link snow algal presence and ecological drivers of blooms (Gray et al. 2021 ), these methods work at lar ge, landsca pe scales, and the fundamental unit of a bloom is microscopic cells .T hough we still hav e v ery little understanding of what contr ols snow algae bloom onset and dynamics, it's v ery likel y blooms may be controlled at m uc h smaller scales (on the order of meters or centimeters), especially when blooms are patchy as is often seen in alpine snow that is not associated with large ice fields/glaciers .T hese types of alpine snow habitats can be particularly complex with many micr ohabitats suc h as tarns , lakes , talus , vegetation, soil, and gr av el, underl ying snow.Though lar ge r emote sensing studies are helpful in predicting the albedo consequences of snow algal blooms, they curr entl y do little to help us understand the biological dynamics that ultimately support the onset of and control blooms, particularly in patchy and complex snow habitats.
The biological dynamics of snow algal blooms can be described in terms of biodiversity , physiology , and ecological dynamics throughout a bloom.While there are upw ar d of 40 described taxa of algae adapted to live in snow (Hoham and Remais 2020 ), ther e ar e onl y a fe w species fr om thr ee gener a -Sanguina (formerly assigned to Chlamydomonas ), Chlainomonas , and Chloromonas that produce visible pink or red blooms in snow.T hus , biodiversity in snow is r elativ el y simple compared to most aquatic systems.Our understanding of snow algae biodiversity has been improving r ecentl y with the use of gene-based surv eys (Lutz et al. 2016, Segawa et al. 2018, Engstrom et al. 2020 ).Biodiv ersity surv eys in Pacific Northwest pink and red snow blooms suggest blooms can be dominated by the snow algae genera Sanguina , Chlainomonas , or Chloromonas and that the composition of blooms changes across ele v ation and season (Mallon 2019, Engstrom et al. 2020 ).The composition of other microbes and microeukaryotes that interact with the algae show can also vary by location and season (Lutz et al. 2016 , Tuc ker andBr own, 2022 ).
Snow algae physiology has been studied across many environments and species using different measures of photophysiology.Photophysiology can be used to tr ac k c hanges in the algae's ability to do photosynthesis through time as environmental conditions shift through the duration of a bloom (Stibal et al. 2007, Tala et al. 2017 ).Pulse Amplitude Modulated (PAM) fluorometry is a r a pid and non-inv asiv e tool has been used extensiv el y thr oughout macroalgal marine environments to study algal photophysiology (Tala et al. 2017, Gao et al. 2018 ) as well as in arctic (Kuhl et al. 2001 ) and alpine algal environments (Kvíderová 2010, Stibal et al. 2007, Pr oc házk ová et al. 2018 ) to addr ess natur al comm unity photophysiology.PAM fluorimetry can be particularly useful when used to measure the maximum quantum yield of PSII (Fv/Fm), whic h is widel y used in study of algae as a metric that can be affected by stress, including envir onmental str ess like temper atur e and light (Sc hr eiber 2004 , Zheng et al. 2020 ).
Snow algae, like all gr een algae, hav e complex life cycles and alternate between stages with different morphologies, and pre-sumabl y differ ent ploidies, using a combination of sexual and asexual (e.g., mitotic di visions) re production that may impact how and when they bloom (Hoham and Ling 2000 ).Often, algae that produce harmful blooms in marine systems will undergo rapid asexual growth to produce high biomass quickly, and then resting spores once resources are exhausted (Smayda 1997 ).Chlainomonas and Sanguina , two of the most common red snow producing taxa, cannot curr entl y be cultur ed in the labor atory.T hus , making connections between life cycle transitions and bloom formation in these taxa is challenging.Snow algal life cycles have been well described in many snow c hlor omonads species, but these taxa are not known to produce large blooms across landscape scales and do typically not produce large red blooms (Hoham and Remias 2020 ).What is known about the life cycles of the red blooming taxa, Sanguina and Chlainomonas , comes primarily fr om field-collected samples.A r ecent study has reported a flagellate stage of Sanguina , but little is known about the life cycle of this abundant snow alga (Raymond et al. 2022 ).Chlainomonas , in contrast, has a dynamic life cycle with man y mor phological sta ges described from field collections from multiple species and found across the world, including North America, Eur ope, Ja pan, and New Zealand (Hoham 1974a, Hoham 1974b, Novis 2002b, Novis et al. 2008, Remias et al. 2016, Pr oc házk ová et al. 2018, Matsuzaki et al. 2022 , Matsumoto et al. in r e vie w).
In addition to c har acterizing the snow algae themselves during a bloom, it is critical to understand how snow habitat changes throughout a bloom cycle and how this impacts bloom dynamics.Man y physioc hemical aspects of snow habitat may impact snow algal biology, including temper atur e , light, and nutrients .T emperature is known to play a role in snow algal blooms gener all y, a gain because of the need for liquid w ater.Ho w e v er, the temper atur e of snow r emains consistentl y at or around 0 • C during blooms, while air temper atur e can fluctuate (Kvíder ová 2010 ).While man y snow algal taxa can survive freeze-tha w cycles , a verage air temper atur es need to be above 0 • C to support growth (Hoham 1975a ).Novis ( 2002a ) observed higher growth rates during rainstorms on snow, wher e melt r ates and liquid water av ailability wer e highest.Light is also an important factor for all photosynthetic organisms.Snow algae are adapted to high-light en vironments; T hey can experience up to 5,000 mol photons m −2 s −1 with changes in light sometimes triggering life cycle transitions (Hoham and Remias 2020 ).Lastl y, nutrients ar e known to be important factors in algal blooms.Sno w is kno wn as a lo w-nutrient environment, and the limited studies of nutrients in the snow habitat are not conclusive as to exactly how natural nutrient levels in snow habitat affect snow algal blooms (Hoham and Remias 2020 ).Ho w e v er, snow algal blooms have been shown to intensify in response to addition of nutrients (Ganey et al. 2017 ).
To address the dynamics of a snow algal bloom in situ under natural conditions, we studied a known r ecurr ent pink snow algal bloom in the North Cascade Mountains (R. K odner, pers .obs .).T he North Cascade Range is a low-ele v ation mountain r ange and is the most glaciated mountain range in the continental US.The range runs north-south through Washington, with high snowpack accumulation and diverse geomorphology.The Cascades are known to host large and diverse snow algal blooms both above and below tr ee line, man y of whic h wer e c har acterized in the 1970s (Hoham 1974a, 1975b, Hoham and Mullet 1977, Hoham and Roemer 1979 ).We have been observing a recurring bloom of Chlamydomonas sp.since 2017 (R. Kodner pers. obs., Matsumoto, et al. in r e vision) on snow on top of Upper Bagle y Lak e in the Mt Baker National Forest, WA.The bloom can be well defined in terms spatially, occurring only on the snow-on-lake habitat, and the end of the bloom is determined by the seasonal loss of the snow habitat by midsummer.This snow algal bloom is an early season bloom (begins with the first spring melts) and is accessible to sampling yearround.
These combined c har acteristics giv e the Ba gle y Lak e Chlainomonas sp.bloom a natural experimental design to observe the spatial and temporal changes during the bloom cycle from the start of visual surface biomass through the duration of habitable snow in the Bagley Lakes system.Our objective in this study was to c har acterize Chlainomonas sp.bloom dynamics by integr ating measur ements of envir onmental conditions (temper atur e, light) of the habitat, observations of community structure (using microscopy and amplicon metabarcoding), photophysiology, and snow surface color that marked the observable "bloom" on snow surfaces .T hrough this study design, we were able to address how the micr obial comm unity composition, algal physiology, and color intensity of snow surfaces as the bloom pr ogr essed ov er space and time.

Study site
Upper Bagley Lake is in the North Cascades mountains of western Washington State .T he lake (1277 m abo v e sea le v el) sits 11.8 km northeast of Mt. Baker at 48.8632, −121.6793 (Fig. 1 ).The lake is situated in a basin that is flanked by Mt Herman to the north and Table Mountains to the west.During the late autum -early summer, Bagle y Lak e is typicall y cov er ed in snow and is surr ounded b y sno w cov er ed slopes.This ar ea r eceiv es dir ect sun fr om earl y morning through late afternoon.Bagle y Lak e is unique in that it does not ice over as many alpine lakes do in winter.Instead, fall snowpack begins to accumulate in somewhat mild temperatures that hov er ar ound fr eezing, so that the lake surface itself does not freeze .T he lake is also shallow, with depths that range from 2-3 m in the summer.T hus , the lake water is absorbed into the snowpack as it accumulates in the winter, and floats over a thin layer of slush and water at the lake bottom until the spring melt.The snowpac k that cov ers the slush mixtur e is r oughl y 0.5 m deep before the slush transition in the spring immediately preceding the bloom and becomes shallo w er over the spring to summer transition before the snow on the lake melts completel y.Ev en to w ar d the end of the bloom where open water is found on most of the perimeter of the lake, the large residual snow rafts are supportive and can be walked on.
The snow algal blooms in the basin appear first on the snow that covers Upper Bagley Lake and persist on the snow-on-lake habitat until snow completely melts off the lake.Complete snow melt on the lake surface r egularl y occurs in the middle of July but can be as early as mid-J une .T he onset of the bloom ranges from late April to late May (R. Kodner, pers.obs.) and continues through mid-summer until the snowpac k disa ppears fr om the surface of the lake.Snow algae often initially appear at the north side of the lak e, suggesting the y may be responding to the south facing light envir onment.Typicall y, by the end of the bloom on Bagley Lake, snow algae can be seen in the surrounding snow slopes, but not before, suggesting the snow-on-lake habitat is habitable before the snow over rocks or vegetation.We have observed this pattern of early blooms on the lake, whic h ar e dominated by Chlainomonas sp., e v ery year since 2017.This species is an undescribed species of Chlainomonas (Novis et al. 2023 , Matsumoto et al. in review).This species represents over 98% of amplicons from lake samples from 2017 to 2019 (R. Kodner, unpublished data) and in all microscopy samples collected from 2017 to 2023 (R. Kodner, unpublished data).In 2021, we identified the start of the Chlainomonas sp.bloom by the a ppear ance of pink patches on 20 May and sampled weekly until 9 July.

Sample collection
To ca ptur e the spatial variability of the bloom across the lake, we set up a sampling grid at 60 m interv als acr oss the lake using Ar cGIS (Fig. 1 ).Point A w as the first observed instance of snow algae (pink patches) at T o (Week 0) and was found outside the pre-determined grid.Sample locations B-H were then determined from the grid and sampled starting T 1 when snow algae patches wer e found acr oss the lake .T his grid served as the scaffolding for all measurements and samples across the season.Samples were collected for light and electron microscop y, DN A extraction, and r elativ e c hlor ophyll fluor escence anal ysis at eac h sample location.

En vironmental da ta
We monitor ed temper atur e and light conditions on the bloom from 21 May to 9 July 2021 using three HOBO loggers set across the lake at sample locations A, B, and G. Loggers were mounted flat, facing the sky, on white PVC pipes and took readings every five minutes.As the snowpack melted throughout the bloom, we repositioned the loggers each week to k ee p them as close to the snow as possible.Additionally, we gathered air temperatures from the surrounding area from the Northwest Avalanche Center (NWAC 2022 ) station located at Heather Meadows (ele v ation 1283 m).We summarized maximum and minimum daily temperatures for each day on the bloom as an av er a ge of the three loggers ( + / − SD).The temper atur es fr om the NWAC station were singlepoint values with no measure of spread.We summarized the light condition of the bloom as maximum daily light averages for the three loggers.We then av er a ged the maxim um dail y light v alues (n = 3 + / − SD) for the duration of the bloom.

Microscopy
At each site (n = 8 per w eek), w e collected 15 ml of fresh pink snow from within a 5 m radius of the site pin and stored the samples in an insulated cooler packed with snow to keep cold without freezing.All samples were k e pt in the dark and remained as snow, and wer e ima ged between 12 and 48 after collection.To ima ge, 20-30 ul of liquid water from the sample that began to melt at ambient temper atur es was added to a slide.We took 20 images from each of the eight samples at 400x total magnification (EVOS XL Core Imaging System, Thermo-Fisher, Waltham, MA, USA), and visually assessed the cell structure and morphology.Samples for scanning electr on micr oscop y (SEM) w er e collected fr om sno w or meltw ater.Biomass from samples dried on circular cover slips and coated with gold and palladium and imaged with a Tescan Vega 3 with Oxford EDS (Brno, Czech Republic) SEM.

DN A extr action and amplicon sequencing
For DNA collections, ∼ 20 ml of snow water equivalent was added to a 50 mL conical vial and mixed into 20 ml RNA buffer pr eserv ati ve lik e RN Alater (935 ml MilliQ w ater, 700 g Ammonium sulfate, 25 ml of 1 M Sodium Citrate, and 40 ml of 0.5 M EDTA, adjusted to pH 5.2 and autoclaved).Samples were stored at 4 o C before being filtered onto 25 mm 0.2 μm Supor filters (P all, Ne w York, USA).Prior to extraction, samples were washed three times with PBS buffer.Filters were cut with sterile scissors, tr ansferr ed to bead beating tubes from Zymobiomics kit (Zymogenetics , Seattle , USA) DNA minipr ep extr actions kits and subjected to bead beating for DN A w as quantified using a Nannodrop to ensure extractions were successful.We submitted DNA to the University of Minnesota Genomics Center for amplicon library prep and sequencing of the V9 region of 18S SSU rRNA gene sequence using MiSeq Illumina 2 x 300 bp c hemistry.UMGC pr epar ed dual indexed Nextera XT DNA libraries following their improved protocol for library preparation which enables detection of taxonomic groups that often go undetected with existing methods (Gohl et al. 2016 ).The V9 region of the 18S rRN A w as amplified using primers Euk1391F 5 -GTA CA CA CCGCCCGTC-3 and EukBr 5 -GATCCTTCTGC AGGTTC ACCTAC-3 (Amaral-Zettler et al. 2009, Stoek et al. 2010 ) and the ITS2 region was amplified using primers Coleman c 5 -GCA TCGA TGAA GAA CGCA GC-3 and Coleman b 5 -GGGA TCCA T A TGCTT AA GTTCA GCGGGT-3 (Coleman et al. 1994, Segawa et al. 2018 ).UMGC pr epar ed dual indexed Nexter a XT DNA libraries following their improved protocol for library preparation which enables detection of taxonomic groups that often go undetected with existing methods (Gohl et al. 2016 ).OTUs were assembled from the V9 reads using mothur following following the MiSeq SOP (Kozich et al. 2013 ).Amplicon sequence variants (ASVs) wer e r ecov er ed fr om the ITS2 data using D AD A2 (Callahan et al. 2016 ) following the SOP at https://benjjneb .github.io/dada2/.The top 24 amplicons identified, whic h r epr esent at least 90% of all sequence reads in the most samples, were then annotated with BLAST using nr from NCBI.Information of r efer ence hits to all algae and chytrid taxa found in Supplement 2 .

Relati v e c hlorophyll fluorescence
We measured the maximum quantum yield of PSII (Fv/Fm) of the bloom each week using an Opti-Sci OS1p field PAM fluorometer with a phytoplankton cuvette (Hudson, NH, USA).We took all readings between 10:00 and 12:00.All samples had similar light histories from the time of collection to measurement.Following the preset sampling grid ((n = 8 per w eek)), w e collected 50 ml of pink snow in Falcon Tubes from each site within a 5 m radius of the site marker/GPS waypoint.We then allowed snow to partially melt into a slurry (10-15 minutes), allowing some algal cells to settle to the bottom of the tube.We collected algae from the bottom of the Falcon tube and tr ansferr ed thr ee 1.8 ml aliquots to the measuring tubes.Samples were loaded into the phytoplankton cuvette and dark adapted for 10 minutes.Following dark adaptation in the aquatic cuvette, we measured the algae's response to light according to the OS1p manual.We adjusted the modulation intensity of the actinic light prior to measurement to ac hie v e a baseline fluorescence (F t ) high enough for a successful reading.We used a saturation light pulse length of 1.5 s to achieve enough fluorescence for an accurate reading, which differed from previous r esearc h (Shr eiber 1995 ).T his optimized pulse length ga ve us a clear fluorescence peak with a flat top compared to a weak and inconsistent peak with shorter satur ation dur ations (Roseqvist and van Kooten 2003 ).

Cell density
We calculated algal cell densities of the samples used in PAM fluor escence anal ysis in weeks 2-6.A 1.8 ml aliquot were collected dir ectl y fr om the measuring tubes and were fixed with 2% paraformaldehyde immediately after fluorescence measurements.Fixed samples were then counted in a hemocytometer chamber using light microscopy (as described above).We quantified cells from ten 1 x 1 mm boxes, from two pr epar ed slides per pr eserv ed sample .T he a v er a ge number of cells per ml was calculated from the technical replicates.

Color intensity of bloom patches
To quantify the intensity of color on the snow each week, five repr esentativ e patc hes of algae wer e selected at eac h site and photogr a phed for ima ge anal ysis.To standardize ima ging of the snow algal patches, we built an imaging stand out of PVC pipe with camera holder on top of a 0.5 x 0.5 m square quadrat (shown in Supplement 1 ).The camera was held in a camera holder on top of 1.5 m arms, standardizing the imaging distance and camera orientation.At each selected patch, the PVC quadrat imaging stand was le v eled on the snow surface surr ounding the patc h to cr eate a 0.5 m square border.A digital SLR camera (HD-45002 DSLR mark II Cannon) was placed on the top of the imaging stand with the lens facing dir ectl y to w ar ds the sno w using auto focus mode.We took thr ee r eplicate ima ges of eac h patc h (n = 15 ima ges per site) and best the photo was used for quantification.The imaging stand and observer were positioned to minimize shadows.
We quantified the extent of pink snow cov er a ge in ima ges via analysis of pixel intensity using FIJI-ImageJ (Abràmoff 2004 ).Each image was processed with the following protocol in ImageJ: We selected a square within the borders of the PVC pipes that included as m uc h interior space as possible.Within the Color Threshold window and with a red threshold, we set the hue between 215 and 255 while saturation and brightness were adjusted to o verla y all areas determined to show pink snow in the image .T he area cov er ed by the threshold was selected with "select" in the Color Threshold window.We then set the measuring stats using "Set Measur ements".These included: ar ea, min and max gr ay v alue, integrated density, and mean gray value.We measured the area with "measure" to give us the values for the selected pixel area.We then duplicated the selected square for each photo to include only snow within the quadrat perimeter.This a ppr oac h ensur ed the color threshold analysis was performed on the same square of snow within the PVC quadrat for each image .T his method also eliminated measuring any snow outside the quadrat area.All area v alues fr om quadr ats fr om individual site were then av er a ged, giving us eight individual final values per week.We used the integrated density values as the best measure of color intensity.These intensity values are scale-independent and unitless, and compar able acr oss all ima ges in the pr oject.The individual pictur e scaling allo w ed us to compar e v alues among pictur es without the need for external comparisons to other bloom situations.

Qualitati v e assessment of bloom patchiness across the lake (visual survey)
We assessed the extent and intensity of the algal bloom across Bagle y Lak e each week with a systematic visual survey of the sampling area using a team of observers .T hese observations were taken with the naked eye, and estimates of intensity was cali-brated to standardize observations among observers each week.Using the established the sampling points (B-H) that marked a 6x6 120m 2 grids (Fig. 1 ), we divided each internal square into a grid that was two to thr ee squar es wide and 10 squares (10 m each) long, giving us 20-30 pixels.Two to thr ee observ ers sim ultaneousl y moved in parallel across the bloom in either north or south directions.After 10 m, the observers stopped and quantified the intensity of the bloom using a 0-4 scale (0 = none, 1 = r ar e , 2 = sparse , 3 = common, 4 = abundant).Individual observers calibrated their assessment of color scoring after the first pass.An independent observer coordinated the movement of observer team and observ ations and r ecorded data.After r ecording a spot, all observ ers r epeated the 10 m pr ogr ession.We omitted observations when we encountered either debris or surface water/open water that made the visible presence of algae impossible, thus the number of observ ations wer e r educed eac h w eek as the sno w on the margins of the lake melted.No observations were made during week 3 due to snowfall the night before (thus difficult to observe the patches) or during week 8 due to degradation of the snowpack.We then tr ansposed the observ ations into Excel to create heat maps using conditional formatting for color gradation (0-4) for cells that matc hed the observ ation grid.Darker colors r epr esent mor e intense algal pr esence.Blac k squar es on the map indicate that no measurement was taken for that square.

Sta tistical anal yses
We ran all statistical analyses in R ver.3.3.0(R Core Team 2022 ).We ensured that the assumptions of normality and equal variance were met prior to statistical testing.If data did not initially meet the necessary assumptions, we used log-transformations.Unless otherwise stated, all tests used an α = 0.05 to determine the presence of significant patterns.We quantified differences in Fv/Fm values of the snow algal biomass among observation weeks using a one-way ANOVA with week as a fixed factor (n = 8 per week).We compared the visual density of snow algae among weeks on log-tr ansformed v alues with a one-w ay ANOVA with w eek as the fixed factor (n = 8 per w eek).Similarly, w e compared the relative proportion of Chlainomonas sp. in the microbial assemblage in 18S SSU rRNA amplicon sequence data with a one-way ANOVA among weeks.Week 0 was k e pt in the gr a phical summary as a r efer ence for the population at the onset of the bloom.Finally, we compared cell counts of samples used for fluorescence analysis with a oneway ANOVA on log-transformed cell counts (n = 8 per week).Any ANOVA that was found to be significantly different ( P < 0.05) was follo w ed b y a Tuk e y post -hoc test to determine pairwise differences among the treatment levels.A correlation test was used to assess the relationship between Fv/Fm and integrated density where data was available from both measurements (n = 6).

Environmental conditions
Minim um av er a ge dail y snow surface temper atur es r anged fr om −4.8 to 3.6 o C while atmospheric temper atur es r anged fr om 1.7 to 29.7 o C. Temper atur es measur ed on the bloom wer e consistently lo w er than atmospheric temper atur es at Heather Meadows (Fig. 2 A) but had similar patterns tempor all y.Ther e was little daily variation in temperature among the three corners of the study area.Washington State experienced a heat dome during the last week of June 2021.This coincided with the sixth week of bloom monitoring at Bagley Lake.During that time, both ambient and snow-surface temper atur es incr eased with a record ambient

Light microscopy
All pink snow samples that were imaged were dominated by Chlainomonas cells.Chlainomonas species display morphological v ariability acr oss life sta ges (Hoham 1974a, Hoham 1974b, Novis 2002b, Novis et al. 2008, Remias et al. 2016, Pr oc házk ová et al. 2018 , Matusumoto et al. in r e vie w).All species fr om this genus hav e a set of distinguished from other snow algae species with the large ellipsoidal to nearly spherical shaped vegetative cells and they possessed a pa pilla (thic kened cell walls with flagellar openings at the anterior pole) and a pseudo-papilla (thickened posterior cell wall).This morphology and the variation in size, cell wall, and papilla or pseudo-pa pilla ar e see in Fig. 3 A-F.The Chlainomonas species that dominates Upper Bagley Lake is a new species and is larger than Chlainomonas rubra , though it nests within other sequences identified as C. rubra (Novis et al. 2023 , Matsumoto et al. in r e vie w).
Chlainomonas sp. common cell morphologies observed during the 2021 are similar to other described species of Chlainomonas and the common morphologies (Fig. 3 A-E), including the unique cytoplasm extrusion, described by others as cell division (Novis et al. 2008 ) seen in the middle cell in 3D.Morphologies show in Fig. 3 A-E wer e observ ed thr oughout the bloom and acr oss the lake.Less common cells types, including Fig. 3 G and was seen in weeks 3-6, and Fig. 3 C which was more seen in the early weeks of the bloom.During and immediately after the heat wave event in week 6, we observed cells with spore-like walls clumping into rafts (Fig. 3

F).
A spor e mor phology is shown in Fig. 3 F, in r elation to v egetativ e cells and Fig. 3 H (see Supplement 2 for SEM image of spores).
Infection of the algal cells by a chytrid fungus was also frequentl y observ ed (Fig. 4 ).Infected cells were seen in e v ery week of the bloom, but the most infected areas of the lake were not consistent over time. in addition, the fungus Chionaster nivalis (Fig. 4 A) was present in each week of the bloom.Two distinct Infection morphologies wer e observ ed (r ound and triangular) and often se v er al fruiting bodies can be seen on cell surfaces (Fig. 4 B-I).In some cases, the infection can be seen penetrating the cell wall of the algae (Fig. 3 E-I).Fungal or chytrid hyphae can also sometimes be seen (Fig. 3 H).On se v er al occasions, we observ ed l ysing e v ents, particularly with larger cells once under the microscope (Fig. 4 B).Lysing is distinct from Chlainomonas 's unique cell division where cytoplasm is extruded out of a v egetativ e cell to produce a new cell enclosed in a cell membrane (Fig. 3 D middle cell) (Novis et al. 2008 , Matsumoto et al. in r e vision).This cell division was observed in weeks 3-6 and was observed in an infected cell (Fig. 4 F) suggesting that infection does not prohibit cell division.

Amplicons for community composition
Amplicon data from the V9 18S SSU rRNA gene sequence was used to assess total eukaryote composition from samples collected across the bloom.Data are available via NCBI BioProject PRJNA985372.V9 18S SSU rRNA gene Amplicon data show that all samples are dominated by Chlainomonas sp.(red taxon, Fig. 5 ) except for a few weeks toward the end of the bloom when samples became fungal dominated.Ther e ar e 4 unique amplicon sequence variants that have a best match to Chlainomonas sp., but the ov erwhelmingl y dominant ASV (97.3% of all ASVs annotated to Chlainomonas ) has a 100% identity to full length Chlainomonas sp. that appears to be a new species in comparison to the described Chlainomonas species described from New Zealand, Japan, and Europe (Matsumoto, et al., in r e vie w).All 4 amplicon sequence v ariants hav e the same BLAST hit r esults for r efer ence sequences ( Supplement 3 ).We used the ITS2 amplicons to assess more specific diversity within the dominant Chlainomonas clade across the bloom.We r ecov er ed onl y a single ITS2 sequence (OP297764.1)that was annotated as Chlainomonas sp.across all samples .T his sequence also supports the identification of the Bagley Chlaionmomons as a new species (Novis et al. 2023 ).The r elativ e pr oportion of Chlainomonas sp.measured with the V9 amplicon was constant over the bloom period until the final week where the proportion was significantly higher (F 5,41 = 9.49, P < 0.001) (Fig. 5 ).
Patterns in other taxa can be also seen over the course of the bloom.Conifer DNA appears in the data early in the bloom season, ciliates are found mid-bloom, where populations may grow in r esponse to incr ease algal biomass.Chytrid and fungal taxa (ASVs binned by functional groups) remain relatively stable through the bloom, except for some fungal dominated samples at the end of the bloom, but this was not consistent in e v ery sample.Chytrid infection was observed thought the bloom in the microscopy data (Fig. 4 ) and amplicons are present through the entirety of the bloom as well.Replicate samples are shown to indicate patch level v ariability acr oss the bloom.Eac h of the two abundant c hytrid amplicon sequence variants hit different sets of reference sequences .T he dominant ASV has a 100% match to an environmental sequence from the Swiss Alps (accession AJ867631.1)and the next most abundant chytrid sequence's best match to a reference is only at 98.4%.Uncultured Chrysophytes (likely a single taxon r epr esented b y 3 ASVs) w ere also common in the amplicon samples in weeks 1-5, though were not observed commonly in microscopy samples.Chrysophytes likely have a higher DNA extraction efficiency from mixed community samples than Chlainomonas sp .that has an extr emel y thic k cell wall that is hard to break in single cell PCR attempts and is difficult to penetrate with DAPI, a nuclear stain that is often easil y penetr ates cell walls (unpublished).This taxon has been observed in other years and can produce localized y ello wish-bro wn blooms on Upper Ba gel y Lake, but did not bloom in 2021.Ciliates and Chrysophytes, and to some extent Chytrids, had a reduction in relative proportion during and following the heat dome in week 6.

Chlorophyll fluorescence measurements
The mean Fv/Fm of the snow algal community in the Bagley Lake bloom ranged from a high of 0.62 during week one to a low of 0.20 during week 6 (Fig. 6 A).The Fv/Fm values significantly decreased across weeks during the 2021 bloom (F 7,45 = 22.63, P < 0.001) (Fig. 6 A).Fv/Fm (or the maximum quantum yield of PSII) was highest during the first and thir d w eeks of the stud y (Tuk e y HSD, P < 0.05) and is consistent with the highest measurements for Fv/Fm that we measure for snow algae that we see in this region (Kodner unpublished data).Fv/Fm then declined to a plateau during week 4 of the study to value that indicate continued photosynthetic activity in the algae but some le v el of str ess in PSII.This plateau was consistent through to the end of the study except for week 6, where we saw lower fluorescence values drop to their lo w est le v els (Tuk e y HSD, P < 0.001).

Intensity of bloom patches over time (image quantification and cell counts)
The cell density of algal samples collected from the same sampled used for r elativ e c hlor ophyll fluor escence measur ements wer e similar among weeks 2-6 (Fig. 6 B).Cell densities r anged fr om 200 to 10 300 cells ml −1 .Algal patch color intensity on the snow surface (reported as integrated density) increased significantly from beginning of bloom to the end (F 5,41 = 5.38, P < 0.001) (Fig. 6 C).Integrated algal density in week 6 was significantly higher than weeks 1, two and five (Tuk e y HSD, P < 0.05), coinciding with decreases in Fv/Fm that week, but not a decrease in cell density.The log of the Integrated density and Fv/Fm is are negatively correlated (with a ρ = −.91,P value = .011)suggesting a significant inverse relationship between these measurements during this bloom.Howe v er, this anal ysis included onl y a small number of measurements (n = 6) and is suggestive rather than demonstrative.

Visual survey of bloom patchiness across the lake
The observ ational tr ansect surv ey of color across the surv ey ar ea each week throughout the bloom sho w ed heterogeneity with no clear visual pr ogr ession among weeks (Fig. 7 ).We observed patch intensities from 0 (no color) to 4 (darkest pink) in all survey weeks, across the study area.The intensity of a given area shifted weekly within each sampling area in the survey grid as well as across the whole study area.The snow melted on the lake surface as the season pr ogr essed, leading to a smaller ov er all ar ea av ailable for algae to grow and thus a smaller survey area each week.This loss of habitat was on the east side of the lak e, with ad ditional losses along the north side (Figures 1 and 7 ).

Discussion
This study aimed to understand the spatial and temporal dynamics of a recurring alpine snow algal bloom.We characterized this bloom weekly over the growing season on Bagley Lake, which lasted for eight weeks in the spring of 2021.We found that the bloom de v eloped in patc hes acr oss the lake area and snow algal cell abundance to be variable but on a verage , constant across the season with no spatial or temporal trends.Despite the consistency in cell abundance, the color intensity of individual bloom patches generally increased throughout the bloom with a heterogeneous patchy distribution that also had no spatial pattern.Chlainomonas sp.consistently dominated the eukaryotic community during the bloom where cell types were mixed thr oughout the patc hes .T his suggests that life cycle de v elopment does not a ppear sync hr onized spatiall y or tempor all y either.Ho w e v er, the Fv/Fm of communities steadily decreased throughout the bloom season, suggesting some le v el of comm unity-le v el stress that is independent of cell abundance or life-cycle but is inv ersel y r elated to color intensity, whic h is a measur e of bloom intensity on snow surfaces .T he patterns we observed of Chlain-omonas sp.blooms on Upper Bagley Lake indicate that, at least for this system, the broad-scale bloom dynamics are starkly different from those in well studied in marine environments (Townsend et al. 1994 ;Strom et al. 2001 ;Sv edrup 1953 ), wher e w e w ould see exponential growth followed by decline and sync hr onized germination.

Environmental sources of stress
Algae in this system could be experiencing str ess fr om se v er al sources including environmental stresses and biological stresses like infection or competition.Snow algae experience environmental str ess fr om m ultiple physio-c hemical par ameters, including water, temper atur e , light, and nutrients .T he impact that en vironmental conditions like light and temper atur e hav e on the photosynthetic capacity and growth of snow algae may dir ectl y impact the longevity of the snowpac k envir onment.Higher av era ge Fv/Fm v alues at the beginning of the bloom suggest that the Chlainomonas sp. was experiencing more optimal conditions and thus less stress than at the end of the bloom.While light and temper atur e ar e centr al to photosynthetic pr ocesses (Remias et al. 2005, Stibal et al. 2007 ), it is ov erl y simplistic to think they alone driv e pr oduction.A combination of light, temper atur e ma y ha ve supported optimal conditions, and thus adequate snow water equivalency (Hoham andRemias 2020 , Remais et al. 2016 ), as well as available nutrients (Jones 1991 , Hoham andLing, 2000 ).We see a decrease in Fv/Fm during the heat dome at the end of June (Fig. 2 ).While daily maximum temperatures on the snow surface remained consistent during this study as measured with environmental sensors on snow surface, ambient air temper atur es in the region spiked over a 7-day period, coinciding with significantly lo w er av er a ge Fv/Fm measur ed fr om in situ samples measur ed during this heat e v en (Fig. 6 A).This dip indicates the algae were experiencing stress, though the exact mechanism for the stress is unclear.The snow surface temper atur e should be near 0 • C in all melting snow en vironments .Howe v er, the snow temper atur e can increase above 0 • C and even small fluctuations could cause stress .We ha ve measured snow temperatures of + 0.2-0.3• C in the snow on Upper Bagley Lake on a more "typical" warm day with air temper atur es r anging fr om 12 to 18 • C (unpublished), so it's possible that temper atur es up to 34 • C raised the snow water temper atur e enough to cause incr eased str ess though measures of snow water temper atur es with a precision of a tenth of a degree were not available in this study.Chlainomonas cells are known to be very sensitive to warming, and flagellate cells can loose their flagella immediately upon warming under the micr oscope, whic h is another indicator of stress (Hoham 1974a , K odner pers . obs .).
Near-fr eezing envir onments like snowpac k ar e limited by liquid water (Hodson et al. 2008 , Anesio and Laybourn-Parry 2012 ), and Chlainomonas species prefer a wetter environment than some other snow algae (Remais et al. 2016, Novis 2002a, Novis et al. 2008 ) including very w et sno w-on-lake (slushy) habitats (Pr oc házk ová et al. 2018, Remias 2011 ).Snow water equivalence of the snowpack supplies the aquatic medium in which snow algae live (Jones et al. 2001, Kuhn 2001 ).It is in this ephemeral water that the algae grow and r epr oduce.Inter estingl y, as a bloom increases in intensity, the dark color on the snow surface reduces albedo, thereby leading to a wetter snow habitat, as positive feedback.Novis ( 2002a ) observed Chlainomonas kolii communities increasing in cell abundance in response to rain on snow e v ents, whic h incr eased the water content of the snow but would also deli ver n utrients.
We did not see ov er all incr eases in cell abundance though we did see an increase in snow color intensity in 2021.It can be challenging to quantify the abundance of algae in snow, especially in patchy blooms where density will be dependent on where algae are collected and how much snow is collected across heterogeneous patches that can vary in color and thus cell density, on millimeter scales.Observed increases in cell density or snow color intensity can be a function of population growth or snow ablation and cell concentration.Our findings suggest increases in color intensity are due to concentration and not increases in ov er all population.The Upper Bagley Lake bloom in 2021 experienced only one pr ecipitation e v ent (sno w in w eek 2) and gener all y sunn y and warm conditions, compared to typical Pacific Northwest spring weather in which Bagley Lakes area experiences regular rain on snow e v ents.
We could not sample for nutrients through the 2021 bloom, because COVID-19 restrictions impacted our study design and

Biological sources of stress
Ther e ar e also biological sources of potential stress to the algae and control of bloom dynamics.Because Chlainomonas sp. is the dominant taxon and overwhelming the dominant algal taxon, competition with other algal species in not an issue, as it often is in aquatic systems.A major source of biological stress may be infection.Chytrid fungal infections of algal cells were visible throughout the bloom, including on the first day of sample collection when only a few small pink patches were visible on the snow surface.Chytrids are known to occur in arctic , alpine , and glacial environments in heterogeneous patterns (Brown et al. 2015(Brown et al. , K oba yashi et al. 2023 ) and likely rely on snow algal cells for nutritional support.The presence of chytrid fungal cells in phytoplankton blooms can be present in epidemic proportions, howe v er, the extent of infection within populations depends on a suite of environmental conditions (Ibelings et al . 2004 ).We observe chytrids in the population through the duration of the bloom.T hus , it is unclear if and how these infections may relate to temporal patterns we observed in algal stress, as measured by photophysiology.
Many of these snow chytrids are novel species (Naff et al. 2013 ).The dominant chytrid ASV in this study was also observed in an unpublished study of snow from a lake in the Swiss Alps (GenBank Accession AJ867631.1)suggesting that this sequence was deriv ed fr om white snow, not pink snow.Tucker and Brown ( 2022 ) also found high abundances of chytrids in white snow, suggesting these organisms also persist in white snow, and their populations may not be dependent on algal blooms.That said, we commonl y observ ed c hytrid cells attac hed to Chlainomonas sp. cells in Upper Bagley Lake .The snow microbiomes in the British Columbia coast range, just north of the Ba gley ar ea, also found an infected Chlainomonas cell (Yakimovich et al. 2020 ).So, chytrid infections in this taxon may be common in the Pacific Northwest and/in the wet snowpack habitat It has also been shown the chytrids can infect S. nivaloides (Tucker and Brown 2022 ) and so the relationship may be a common and perhaps a fundamental aspect of algal blooms.Tucker and Brown ( 2022 ) did see a higher abundance of chytrids in Cascades samples than in the Rockies, also possibly suggesting a relationship with wet snow.
It is not clear if life cycle dynamics do played a role in the changes in seen in Fv/Fm.Chlainomonas spp.have complex life cycles, although the life cycle pr ogr ession is not fully characterized for any species in this genus (Novis et al. 2008, Pr oc házk ová et al. 2018, Remias et al. 2016 ).Because no species from this genus have been successfully cultured, connecting life stages and cell morphologies into a life cycle has been challenging.Like other studies, we observed a heterogenous mixture of Chlainomonas sp.cell mor phologies thr oughout the bloom.Sur prisingl y, despite unsync hr onized life cycle de v elopment, ther e was a significant tempor al decr ease in Fv/Fm in a ggr egate fr om bloom samples acr oss the lake .T his suggests that the entir e Chlainomonas sp.comm unity across the lake experiences similar stress that caused the decrease in photosynthetic capacity, regardless of whether the cells ar e v egetativ e, dividing, or encysted.

Bloom origination
Upper Bagley Lake, unlike other alpine lakes, does not fully freeze over in winter, with ice on the surface and liquid water underneath.Instead, the snowpack extends to the lake bottom where it is slushy.One hypothesis for bloom origination is that the algae colonize the snow surface from the sno w/w ater slush underneath the snowpack by swimming in flagellated stages (Hoham and Ling 2000 ).Supporting this hypothesis, we found Chlainomonas sp.spores in lake sediments (Matsumoto et al. in r e vision).If these algae are swimming up through the snowpack throughout the growing seasons, pinker snow later in the bloom could result from r epr oduction on snow surfaces and or continued vertical migration of cells.Ho w e v er, incr eased snow ablation as the season progresses (Jones 1991, Kuhn 2001 ) would also concentrate cells on the snow surface, leading to more intense coloration on the snow.In this case, ablation alone would not account for the continued patchiness of the bloom, which changes over time, and the unpredictability of the a ppear ence of ne w patc hes.A second bloom origination hypothesis is that Chlainomonas sp. cells are airborne and deposited on the snow's surface (Smith 2011 ).Airborne deposition of algal cells would be a radom process, thus also leading to the patchy distribution of the bloom.The proportional consistency of Chlainomonas present in the 18S SSU rRNA community sequences suggests the source of all microbial depositions is the same, or that the surface environment selects for a consistent microbial comm unity acr oss the lake .T his consistency leads us to belie v e that the microbial community populating the snow surface on Bagle y Lak e originates from the same source population, whether that be from airborne deposition or vertical migration from the lake bottom.This question is best addressed with populationle v el anal yses using a ppr opriate genetic tools.Our ITS2 data produced only a single Chlainomonas sp.sequence variant (Novis et al. 2023 ), so if there is population-le v el div ersity in this bloom, it is not r esolv ed at the le v el of ITS2.We would need mor e pol ymorphic markers, such as microsatellites or single nucleotide polymorphisms to determine population sources.

Conclusions
Our r esults c har acterize a geogr a phicall y defined snow algal bloom, dominated by Chlainomonas sp., across a seasonal cycle.We found that during blooms of Chlainomonas sp. in Upper Bagley Lake, life cycle is not synced acr oss patc hes.In contr ast, the bloom did show a clear decreasing trend in photophysiology (Fv/Fm) that suggested algae wer e str essed as the season pr ogr essed and responded to an excessive heat event.Additionally, the eukaryote comm unity composition pr ogr essiv el y c hanges ov er the bloom, suggesting ecological interactions between trophic levels and between algae and their parasites may be influenced by environmental stress like temperature.As our climate continues to warm, low-ele v ation snow ecosystems like Upper Bagley Lake will become mor e tr ansient, r educing the gr owing season for these algae , which ma y impact the bloom dynamics and the phenology.In addition, extreme heat events likely to be more common, leading to increased photophysiological stress and unknown impacts on life cycle and may impact the ability of this species to bloom in the future.Systems like Upper Bagley Lake, where the bloom is dominated by one species and the start and end of a bloom can be monitor ed, pr ovide an opportunity to study the complex factors that affect snow algae bloom e v ents in a natural habitat.Further de v elopment of these types of studies will allow us to understand the mec hanistic driv ers of str ess in these ecosystems and help predict how these taxa will respond to future warming.

Figure 1 .
Figure 1.A map showing sampling locations A-H situated on Lower Bagley Lake in the Cascade Range, Washington USA.This map was created in ArcGIS.Inset map shows the broader geographic context for the location of this study.

Figur e 2 .
Figur e 2. En vironmental Data (temperature and light) measured across Bagle y Lak e in 2021.Snow surface temper atur e and light r ecor ded b y 3 Hobo loggers placed across the study site (1310 m) elevation and av er a ged.Ambient air temper atur e r ecor ded b y Northw est Av alanc he Center telemetry station at Heather Meadows (1280 m ele v ation) located a ppr ox. 1 km from center of Upper Bagley Lake.A ) Daily av er a ge (n = 3, means + / − SE) surface snow temper atur es (minim um (blue) ambient maximum ambient air temperature (red)) for the area.B ) Maximum daily light intensity ( μmol m −2 s −1 ) (n = 3, means + / − SE).Vertical blue lines show the location of the atmospheric heat dome that occurred during week 6.

Figure 3 .
Figure 3. Re presentati ve Chlainomonas sp.cell morphologies found throughout the 2021 bloom, all of which have been observed as part of the life cycle (Matsumoto et al in r e vie w).All scale bars are 60 μm.A ) Large Chlaionomonas sp.cell with thin cell wall and visible central nucleus 05/20/21 (Week 0) first visible patches of the bloom; B ) Common non-motile v egetativ e Chlaionomonas sp.cell with thick cell wall 05/20/21 (Week 0) first visible patches of the bloom; C ) Small Chlaionomonas sp.cell morphology with condensed cell contents 05/25/21 (Week 1) from site C; D ) Image of 3 Chlaionomonas sp. cells with morphology and size variation co-exiting along with a cell undergoing the unique cell division from a single patch collected on 07/06/21 (Week 7) from site E; E ) Three common Chlaionomonas sp.morphologies co-existing in a patch collected on 06/16/21 (Week 4) from site G; F ) Rafted Chlaionomonas sp.spore morphologies co-existing with the common vegetative cell morphology in a single patch collected on 06/22/21 (Week 5) from site D; G ) The r ar e Chlaionomonas sp.morphology with dense cell contents and think orange layer around cytoplasm collected on 07/06/21 (Week 7) from site E; H ) A single Chlaionomonas sp.spore morphology showing thickened cell wall collected on 07/13/21 (Week 8) from site C.

Figure 5 .
Figure 5. 18S V9 amplicon data from each sample, by week.ASVs assigned to the same taxonomic group are binned and the n is of ASV summed in that group.If no n is listed, only a single ASV contributed group.Vertical blue lines show the location of the atmospheric heat dome that occurred during week 6.

Figure 6 .
Figure 6.A ) Maximal photochemical efficiency of PSII (Fv/Fm).Measurements taken each week of 8 weeks.B ) Cell counts of snow used to measure c hlor ophyll fluor escence (cells/ml).Measur ements taken weeks 2-6.C ) log-tr ansformed integr ated density of color of pink patc hes on surface snow.Measurements taken weeks 1-2, and 4-7.Week 3 was omitted due to recent snow fall that obscured surface patches.Letters represent significant differences among weeks in each panel determined with an ANOVA and Tuk e y post-hoc analysis ( P < 0.05).Vertical black lines show the location of the atmospheric heat dome that occurred during week 6.

Figure 7 .
Figure 7. Heat map showing bloom intensity across Bagley Lake, Washington State in the spring and summer of 2021.Numbers in the upper-left corner of each panel correspond to sampling weeks.We omitted week 3 from the survey due to snowfall the day before that obscured the bloom.Bloom intensities ranged from 0 to 4. Blacked out areas were omitted from the survey due to either obstructions or lack of snow in later weeks.No survey was done during week eight because the bloom had degraded and could not be traversed.